We demonstrate that a generative adversarial network can be trained to
produce Ising model configurations in distinct regions of phase space. In
training a generative adversarial network, the discriminator neural network
becomes very good a discerning examples from the training set and examples from
the testing set. We demonstrate that this ability can be used as an anomaly
detector, producing estimations of operator values along with a confidence in
the prediction